Automated segmentation of ablated lesions using deep convolutional neural networks: A basis for response assessment following laser interstitial thermal therapy

Background: Laser interstitial thermal therapy (LITT) of intracranial tumors or radiation necrosis enables tissue diagnosis, cytoreduction, and rapid return to systemic therapies. Ablated tissue remains in situ, resulting in characteristic post-LITT edema associated with transient clinical worsening and complicating post-LITT response assessment.

Methods: All patients receiving LITT at a single center for tumors or radiation necrosis from 2015 to 2023 with ≥9 months of MRI follow-up were included. An nnU-Net segmentation model was trained to automatically segment contrast-enhancing lesion volume (CeLV) of LITT-treated lesions on T1-weighted images. Response assessment was performed using volumetric measurements.

Results: Three hundred and eighty four unique MRI exams of 61 LITT-treated lesions and 6 control cases of medically managed radiation necrosis were analyzed. Automated segmentation was accurate in 367/384 (95.6%) images. CeLV increased to a median of 68.3% (IQR 35.1-109.2%) from baseline at 1-3 months from LITT (P = 0.0012) and returned to baseline thereafter. Overall survival (OS) for LITT-treated patients was 39.1 (9.2-93.4) months. Lesion expansion above 40% from volumetric nadir or baseline was considered volumetric progression. Twenty-one of 56 (37.5%) patients experienced progression for a volumetric progression-free survival of 21.4 (6.0-93.4) months. Patients with volumetric progression had worse OS (17.3 vs 62.1 months, P = 0.0015).

Conclusions: Post-LITT CeLV expansion is quantifiable and resolves within 6 months of LITT. Development of response assessment criteria for LITT-treated lesions is feasible and should be considered for clinical trials. Automated lesion segmentation could speed the adoption of volumetric response criteria in clinical practice.